Gestures for Globes Research

Data and Libraries Load

library(tidyverse)
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## ✔ ggplot2   3.5.1     ✔ tibble    3.2.1
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.0.4     
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library(dplyr)
library(car)
## Loading required package: carData
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## Attaching package: 'car'
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## The following object is masked from 'package:dplyr':
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##     recode
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library(ggplot2)
library(lubridate)
library(scales)
## 
## Attaching package: 'scales'
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##     discard
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## The following object is masked from 'package:readr':
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##     col_factor
library(ARTool)

data <- read_csv("study_tasks.csv")
## Rows: 49244 Columns: 35
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr   (9): TaskID, ActionID, distance, direction, complexity, zoomDirection,...
## dbl  (22): UserID, main_translation_x, main_translation_y, main_translation_...
## lgl   (3): rotateGlobeWhileDragging, oneHandedRotationGesture, moveGlobeWhil...
## dttm  (1): Date
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
demographic <- read_csv("final_introductory.csv")
## Rows: 12 Columns: 8
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (7): Timestamp, Academic_level, Gender, Age_group, Exp_ARVR, Globe_usage...
## dbl (1): UserID
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
positioning_NRG <- read_csv("final_positioning_NRG.csv")
## Rows: 12 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Timestamp, Mentally_demanding, Physically_demanding
## dbl (1): UserID
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
positioning_RG <- read_csv("final_positioning_RG.csv")
## Rows: 12 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Timestamp, Mentally_demanding, Physically_demanding
## dbl (1): UserID
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
positioning_preference <- read_csv("final_positioning_comparison.csv")
## Rows: 12 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Timestamp, Positioning_preference, Positioning_feedback
## dbl (1): UserID
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
rotation_OH <- read_csv("final_rotation_OH.csv")
## Rows: 12 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Timestamp, Mentally_demanding, Physically_demanding
## dbl (1): UserID
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
rotation_TH <- read_csv("final_rotation_TH.csv")
## Rows: 12 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Timestamp, Mentally_demanding, Physically_demanding
## dbl (1): UserID
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
rotation_preference <- read_csv("final_rotation_comparison.csv")
## Rows: 12 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Timestamp, Rotation_preference, Rotation_feedback
## dbl (1): UserID
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
scale_MG <- read_csv("final_scale_MG.csv")
## Rows: 12 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Timestamp, Mentally_demanding, Physically_demanding
## dbl (1): UserID
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
scale_NMG <- read_csv("final_scale_NMG.csv")
## Rows: 12 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Timestamp, Mentally_demanding, Physically_demanding
## dbl (1): UserID
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
scale_preference <- read_csv("final_scale_comparison.csv")
## Rows: 12 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Timestamp, Scale_preference, Scale_feedback
## dbl (1): UserID
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
combined_preference <- read_csv("final_outro_comparison.csv")
## Rows: 12 Columns: 6
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (5): Timestamp, Combined_positioning_preference, Combined_rotation_prefe...
## dbl (1): UserID
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
summary(data)
##      UserID          TaskID            ActionID        
##  Min.   : 1.000   Length:49244       Length:49244      
##  1st Qu.: 4.000   Class :character   Class :character  
##  Median : 7.000   Mode  :character   Mode  :character  
##  Mean   : 6.741                                        
##  3rd Qu.:10.000                                        
##  Max.   :12.000                                        
##  rotateGlobeWhileDragging oneHandedRotationGesture moveGlobeWhileScaling
##  Mode :logical            Mode :logical            Mode :logical        
##  FALSE:36803              FALSE:11933              FALSE:46552          
##  TRUE :12441              TRUE :37311              TRUE :2692           
##                                                                         
##                                                                         
##                                                                         
##    distance          direction          complexity        zoomDirection     
##  Length:49244       Length:49244       Length:49244       Length:49244      
##  Class :character   Class :character   Class :character   Class :character  
##  Mode  :character   Mode  :character   Mode  :character   Mode  :character  
##                                                                             
##                                                                             
##                                                                             
##       Date                            Type           ActionStatus      
##  Min.   :2025-04-23 05:27:13.00   Length:49244       Length:49244      
##  1st Qu.:2025-04-25 01:36:58.00   Class :character   Class :character  
##  Median :2025-04-26 00:45:01.00   Mode  :character   Mode  :character  
##  Mean   :2025-04-27 21:46:53.98                                        
##  3rd Qu.:2025-05-01 07:26:51.00                                        
##  Max.   :2025-05-05 23:37:33.00                                        
##  main_translation_x  main_translation_y main_translation_z main_rotation_x   
##  Min.   :-7.099065   Min.   :-0.3298    Min.   :-3.487     Min.   :-0.97540  
##  1st Qu.:-0.400000   1st Qu.: 0.9000    1st Qu.:-1.921     1st Qu.:-0.03161  
##  Median :-0.004060   Median : 0.9000    Median :-1.500     Median : 0.00000  
##  Mean   :-0.005048   Mean   : 1.2326    Mean   :-1.683     Mean   :-0.03896  
##  3rd Qu.: 0.400000   3rd Qu.: 1.5539    3rd Qu.:-1.500     3rd Qu.: 0.00000  
##  Max.   : 3.256168   Max.   : 3.8304    Max.   : 5.006     Max.   : 0.97834  
##  main_rotation_y   main_rotation_z    main_rotation_w       main_scale_x    
##  Min.   :-1.0000   Min.   :-0.97710   Min.   :-0.9997261   Min.   :0.08431  
##  1st Qu.:-0.2033   1st Qu.: 0.00000   1st Qu.: 0.0000001   1st Qu.:0.99989  
##  Median : 0.9601   Median : 0.00000   Median : 0.0626987   Median :1.00000  
##  Mean   : 0.5003   Mean   : 0.01287   Mean   : 0.2756917   Mean   :0.99575  
##  3rd Qu.: 1.0000   3rd Qu.: 0.00000   3rd Qu.: 0.6346812   3rd Qu.:1.00002  
##  Max.   : 1.0000   Max.   : 0.98922   Max.   : 0.9999814   Max.   :7.69231  
##   main_scale_y      main_scale_z     target_translation_x target_translation_y
##  Min.   :0.08431   Min.   :0.08431   Min.   :-3.10000     Min.   :0.613       
##  1st Qu.:0.99994   1st Qu.:0.99990   1st Qu.:-0.40000     1st Qu.:0.900       
##  Median :1.00000   Median :1.00000   Median : 0.00000     Median :0.900       
##  Mean   :0.99577   Mean   :0.99576   Mean   :-0.02449     Mean   :1.245       
##  3rd Qu.:1.00002   3rd Qu.:1.00002   3rd Qu.: 0.40000     3rd Qu.:1.773       
##  Max.   :7.69231   Max.   :7.69231   Max.   : 2.33777     Max.   :2.547       
##  target_translation_z target_rotation_x target_rotation_y target_rotation_z 
##  Min.   :-3.3210      Min.   :-0.3928   Min.   :-0.6935   Min.   :-0.21194  
##  1st Qu.:-1.9598      1st Qu.:-0.3584   1st Qu.:-0.5655   1st Qu.: 0.00000  
##  Median :-1.5000      Median : 0.0000   Median : 1.0000   Median : 0.00000  
##  Mean   :-1.6971      Mean   :-0.1153   Mean   : 0.3768   Mean   :-0.01644  
##  3rd Qu.:-1.5000      3rd Qu.: 0.0000   3rd Qu.: 1.0000   3rd Qu.: 0.00000  
##  Max.   :-0.8953      Max.   : 0.0000   Max.   : 1.0000   Max.   : 0.13795  
##  target_rotation_w    target_scale_x   target_scale_y   target_scale_z  
##  Min.   :-0.9761015   Min.   :0.1700   Min.   :0.1700   Min.   :0.1700  
##  1st Qu.: 0.0000001   1st Qu.:1.0000   1st Qu.:1.0000   1st Qu.:1.0000  
##  Median : 0.0000001   Median :1.0000   Median :1.0000   Median :1.0000  
##  Mean   : 0.2914215   Mean   :0.9946   Mean   :0.9946   Mean   :0.9946  
##  3rd Qu.: 0.7119398   3rd Qu.:1.0000   3rd Qu.:1.0000   3rd Qu.:1.0000  
##  Max.   : 0.9807853   Max.   :2.0000   Max.   :2.0000   Max.   :2.0000  
##  match_accuracy_result    status         
##  Min.   : 0.00000      Length:49244      
##  1st Qu.: 0.00000      Class :character  
##  Median : 0.00000      Mode  :character  
##  Mean   : 0.03784                        
##  3rd Qu.: 0.00000                        
##  Max.   :22.31002
summary(demographic)
##      UserID       Timestamp         Academic_level        Gender         
##  Min.   : 1.00   Length:12          Length:12          Length:12         
##  1st Qu.: 3.75   Class :character   Class :character   Class :character  
##  Median : 6.50   Mode  :character   Mode  :character   Mode  :character  
##  Mean   : 6.50                                                           
##  3rd Qu.: 9.25                                                           
##  Max.   :12.00                                                           
##   Age_group           Exp_ARVR         Globe_usage_frequency
##  Length:12          Length:12          Length:12            
##  Class :character   Class :character   Class :character     
##  Mode  :character   Mode  :character   Mode  :character     
##                                                             
##                                                             
##                                                             
##  Have_used_VisionPro
##  Length:12          
##  Class :character   
##  Mode  :character   
##                     
##                     
## 
summary(positioning_NRG)
##      UserID       Timestamp         Mentally_demanding Physically_demanding
##  Min.   : 1.00   Length:12          Length:12          Length:12           
##  1st Qu.: 3.75   Class :character   Class :character   Class :character    
##  Median : 6.50   Mode  :character   Mode  :character   Mode  :character    
##  Mean   : 6.50                                                             
##  3rd Qu.: 9.25                                                             
##  Max.   :12.00
summary(positioning_RG)
##      UserID       Timestamp         Mentally_demanding Physically_demanding
##  Min.   : 1.00   Length:12          Length:12          Length:12           
##  1st Qu.: 3.75   Class :character   Class :character   Class :character    
##  Median : 6.50   Mode  :character   Mode  :character   Mode  :character    
##  Mean   : 6.50                                                             
##  3rd Qu.: 9.25                                                             
##  Max.   :12.00
summary(positioning_preference)
##      UserID       Timestamp         Positioning_preference Positioning_feedback
##  Min.   : 1.00   Length:12          Length:12              Length:12           
##  1st Qu.: 3.75   Class :character   Class :character       Class :character    
##  Median : 6.50   Mode  :character   Mode  :character       Mode  :character    
##  Mean   : 6.50                                                                 
##  3rd Qu.: 9.25                                                                 
##  Max.   :12.00
summary(rotation_OH)
##      UserID       Timestamp         Mentally_demanding Physically_demanding
##  Min.   : 1.00   Length:12          Length:12          Length:12           
##  1st Qu.: 3.75   Class :character   Class :character   Class :character    
##  Median : 6.50   Mode  :character   Mode  :character   Mode  :character    
##  Mean   : 6.50                                                             
##  3rd Qu.: 9.25                                                             
##  Max.   :12.00
summary(rotation_TH)
##      UserID       Timestamp         Mentally_demanding Physically_demanding
##  Min.   : 1.00   Length:12          Length:12          Length:12           
##  1st Qu.: 3.75   Class :character   Class :character   Class :character    
##  Median : 6.50   Mode  :character   Mode  :character   Mode  :character    
##  Mean   : 6.50                                                             
##  3rd Qu.: 9.25                                                             
##  Max.   :12.00
summary(rotation_preference)
##      UserID       Timestamp         Rotation_preference Rotation_feedback 
##  Min.   : 1.00   Length:12          Length:12           Length:12         
##  1st Qu.: 3.75   Class :character   Class :character    Class :character  
##  Median : 6.50   Mode  :character   Mode  :character    Mode  :character  
##  Mean   : 6.50                                                            
##  3rd Qu.: 9.25                                                            
##  Max.   :12.00
summary(scale_MG)
##      UserID       Timestamp         Mentally_demanding Physically_demanding
##  Min.   : 1.00   Length:12          Length:12          Length:12           
##  1st Qu.: 3.75   Class :character   Class :character   Class :character    
##  Median : 6.50   Mode  :character   Mode  :character   Mode  :character    
##  Mean   : 6.50                                                             
##  3rd Qu.: 9.25                                                             
##  Max.   :12.00
summary(scale_NMG)
##      UserID       Timestamp         Mentally_demanding Physically_demanding
##  Min.   : 1.00   Length:12          Length:12          Length:12           
##  1st Qu.: 3.75   Class :character   Class :character   Class :character    
##  Median : 6.50   Mode  :character   Mode  :character   Mode  :character    
##  Mean   : 6.50                                                             
##  3rd Qu.: 9.25                                                             
##  Max.   :12.00
summary(scale_preference)
##      UserID       Timestamp         Scale_preference   Scale_feedback    
##  Min.   : 1.00   Length:12          Length:12          Length:12         
##  1st Qu.: 3.75   Class :character   Class :character   Class :character  
##  Median : 6.50   Mode  :character   Mode  :character   Mode  :character  
##  Mean   : 6.50                                                           
##  3rd Qu.: 9.25                                                           
##  Max.   :12.00
summary(combined_preference)
##      UserID       Timestamp         Combined_positioning_preference
##  Min.   : 1.00   Length:12          Length:12                      
##  1st Qu.: 3.75   Class :character   Class :character               
##  Median : 6.50   Mode  :character   Mode  :character               
##  Mean   : 6.50                                                     
##  3rd Qu.: 9.25                                                     
##  Max.   :12.00                                                     
##  Combined_rotation_preference Combined_scale_preference Combined_feedback 
##  Length:12                    Length:12                 Length:12         
##  Class :character             Class :character          Class :character  
##  Mode  :character             Mode  :character          Mode  :character  
##                                                                           
##                                                                           
##

Participants Demographic Information

# Total number of participants
length(unique(data$UserID))
## [1] 12
# Participants' gender distribution
demographic.gender <-  demographic %>%
  select(UserID, Gender) %>%
  distinct() %>%
  group_by(Gender) %>%
  summarise(count = n()) %>%
  mutate(percentage = round(count / sum(count) * 100, 1), percentage = paste0(percentage, "%"))

demographic.gender
## # A tibble: 2 × 3
##   Gender count percentage
##   <chr>  <int> <chr>     
## 1 Man       10 83.3%     
## 2 Woman      2 16.7%
# Participants' gender distribution chart
ggplot(demographic.gender, aes(x = "", y = count, fill = Gender)) +
  geom_col(width = 1, color = "white") +
  coord_polar(theta = "y") +
  geom_text(aes(label = percentage), position = position_stack(vjust = 0.5), size = 4) +
  labs(title = "Distribution of Participants' Gender") +
  theme_void()

# Participants' academic level distribution
demographic.academic_level <-  demographic %>%
  select(UserID, Academic_level) %>%
  distinct() %>%
  group_by(Academic_level) %>%
  summarise(count = n()) %>%
  mutate(percentage = round(count / sum(count) * 100, 1), graph_label = paste0(percentage, "%")) %>%
  rename(`Academic levels` = Academic_level)

demographic.academic_level
## # A tibble: 3 × 4
##   `Academic levels`       count percentage graph_label
##   <chr>                   <int>      <dbl> <chr>      
## 1 Graduate Student           10       83.3 83.3%      
## 2 Postdoctoral Researcher     1        8.3 8.3%       
## 3 Undergraduate Student       1        8.3 8.3%
# Participants' academic level distribution chart
ggplot(demographic.academic_level, aes(x = "", y = count, fill = `Academic levels`)) +
  geom_col(width = 1, color = "white") +
  coord_polar(theta = "y") +
  geom_text(aes(label = graph_label), position = position_stack(vjust = 0.5), size = 4) +
  labs(title = "Distribution of Participants' Academic Level") +
  theme_void() 

# Participants' previous AR/VR experience distribution
demographic.ARVR_exp <-  demographic %>%
  select(UserID, Exp_ARVR ) %>%
  distinct() %>%
  group_by(Exp_ARVR) %>%
  summarise(count = n()) %>%
  mutate(percentage = round(count / sum(count) * 100, 1), 
         label = paste0(percentage, "%"),
         ShortLabel = fct_recode(Exp_ARVR,
                          "No experience" = "I have no experience")
) %>%
  rename(`Previous AR/VR experience` = ShortLabel)

demographic.ARVR_exp
## # A tibble: 3 × 5
##   Exp_ARVR                         count percentage label Previous AR/VR exper…¹
##   <chr>                            <int>      <dbl> <chr> <fct>                 
## 1 Beginner (less than 5 hours exp…     4       33.3 33.3% Beginner (less than 5…
## 2 Familiar (5-20 hours experience)     3       25   25%   Familiar (5-20 hours …
## 3 I have no experience                 5       41.7 41.7% No experience         
## # ℹ abbreviated name: ¹​`Previous AR/VR experience`
# Participants' previous AR/VR experience distribution chart
ggplot(demographic.ARVR_exp, aes(x = "", y = count, fill = `Previous AR/VR experience`)) +
  geom_col(width = 1, color = "white") +
  coord_polar(theta = "y") +
  geom_text(aes(label = label), position = position_stack(vjust = 0.5), size = 4) +
  labs(title = "Distribution of Participants Previous AR/VR Experience") +
  theme_void() 

# Participants' previous globe experience distribution
demographic.globes_exp <- demographic %>%
  select(UserID, Globe_usage_frequency) %>%
  distinct() %>%
  group_by(Globe_usage_frequency) %>%
  summarise(count = n()) %>%
  mutate(percentage = round(count / sum(count) * 100, 1),
         graph_label = paste0(percentage, "%")) %>%
  rename(`Previous globes experience` = Globe_usage_frequency)

demographic.globes_exp
## # A tibble: 3 × 4
##   `Previous globes experience` count percentage graph_label
##   <chr>                        <int>      <dbl> <chr>      
## 1 A few times a month              1        8.3 8.3%       
## 2 A few times a year               3       25   25%        
## 3 Once every few years             8       66.7 66.7%
# Participants' previous globe experience distribution chart
ggplot(demographic.globes_exp, aes(x = "", y = count, fill = `Previous globes experience`)) +
  geom_col(width = 1, color = "white") +
  coord_polar(theta = "y") +
  geom_text(aes(label = graph_label), position = position_stack(vjust = 0.5), size = 4) +
  labs(title = "Distribution of Participants Previous AR/VR Experience") +
  theme_void() 

# Participants' previous Apple Vision Pro Experience distribution
demographic.visionpro_exp <- demographic %>%
  select(UserID, Have_used_VisionPro) %>%
  distinct() %>%
  group_by(Have_used_VisionPro) %>%
  summarise(count = n()) %>%
  mutate(
    percentage = round(count / sum(count) * 100, 1),
    graph_label = paste0(percentage, "%")
  ) %>%
  rename(`Have used Apple Vision Pro` = Have_used_VisionPro)
  
demographic.visionpro_exp
## # A tibble: 2 × 4
##   `Have used Apple Vision Pro`                   count percentage graph_label
##   <chr>                                          <int>      <dbl> <chr>      
## 1 I have never used the Apple Vision Pro            11       91.7 91.7%      
## 2 I have used the Apple Vision Pro once or twice     1        8.3 8.3%
# Participants' previous Apple Vision Pro Experience distribution chart
ggplot(demographic.visionpro_exp, aes(x = "", y = count, fill = `Have used Apple Vision Pro`)) +
  geom_col(width = 1, color = "white") +
  coord_polar(theta = "y") +
  geom_text(aes(label = graph_label), position = position_stack(vjust = 0.5), size = 4) +
  labs(title = "Distribution of Participants Previous AR/VR Experience") +
  theme_void()

Notes: Use boxplot instead of bar chart Add technique column

Structure it like this:

Study: Positioning

Positioning Data Preparation

data.positioning <- data %>%
  inner_join(demographic, by = "UserID") %>%
  inner_join(positioning_NRG, by = "UserID") %>%
  rename(
    PAAS_NRG = Mentally_demanding,
    BORG_NRG = Physically_demanding
  ) %>%
  mutate(
    PAAS_NRG = as.numeric(str_extract(PAAS_NRG, "\\d+(\\.\\d+)?")),
    BORG_NRG = as.numeric(str_extract(BORG_NRG, "\\d+(\\.\\d+)?"))
  ) %>%
  inner_join(positioning_RG, by = "UserID") %>%
  rename(
    PAAS_RG = Mentally_demanding,
    BORG_RG = Physically_demanding
  ) %>%
  mutate(
    PAAS_RG = as.numeric(str_extract(PAAS_RG, "\\d+(\\.\\d+)?")),
    BORG_RG = as.numeric(str_extract(BORG_RG, "\\d+(\\.\\d+)?"))
  ) %>%
  inner_join(positioning_preference, by = "UserID") %>%
  rename(
    behaviour_preference = Positioning_preference,
    behaviour_feedback = Positioning_feedback
  ) %>%
  mutate(
      behaviour_preference = case_when(
    str_detect(behaviour_preference, "Static orientation") ~ "staticOrientation",
    str_detect(behaviour_preference, "Adaptive orientation") ~ "adaptiveOrientation",
    str_detect(behaviour_preference, "no preference") ~ "noPreference",
    TRUE ~ "unknown"
  ) ) %>%
  filter(Type == "positionTask") %>%
  select(UserID, TaskID, ActionID, rotateGlobeWhileDragging, distance, direction, Date, ActionStatus, main_translation_x,
  main_translation_y, main_translation_z, target_translation_x, target_translation_y, target_translation_z, 
  match_accuracy_result, status, PAAS_NRG, BORG_NRG, PAAS_RG, BORG_RG, behaviour_preference, behaviour_feedback) %>%
  mutate(positionCondition = if_else(rotateGlobeWhileDragging, "rotatingGlobe", "nonRotatingGlobe")) %>%
  select(-rotateGlobeWhileDragging) %>%
  mutate(distance = as.factor(distance), 
         direction = as.factor(direction), 
         positionCondition = as.factor(positionCondition),
         status = as.factor(status),
         behaviour_preference = as.factor(behaviour_preference))

Position Task Study

### Accuracy
#### Normality
data.positioning.matched <- data.positioning %>%
  filter(status == "Matched")

shapiro.test(data.positioning.matched$match_accuracy_result)
## 
##  Shapiro-Wilk normality test
## 
## data:  data.positioning.matched$match_accuracy_result
## W = 0.97029, p-value = 2.086e-09
hist(data.positioning.matched$match_accuracy_result, breaks = 100,
     main = "Histogram (Zoomed)", xlab = "Accuracy",
     col = "lightblue", xlim = c(0, 0.06))

plot(density(data.positioning.matched$match_accuracy_result), 
     main = "Density Plot (Zoomed)", xlab = "Accuracy",
     col = "blue", lwd = 2, xlim = c(0, 0.6))

# Although the w value is close to 1, the p value is below 0.05 so we reject null hypothesis that the data is normally distributed
# So, we cannot use one way ANOVA, instead, we use Wilcoxon signed-rank test

#### Statistical tests 
data.positioning.matched.accuracy_avg.long <- data.positioning.matched %>%
  group_by(UserID, positionCondition) %>%
  summarise(mean_accuracy = mean(match_accuracy_result, na.rm = TRUE), .groups = 'drop')

data.positioning.matched.art <- art(mean_accuracy ~ positionCondition + (1|UserID), data = data.positioning.matched.accuracy_avg.long)

anova(data.positioning.matched.art)
## Analysis of Variance of Aligned Rank Transformed Data
## 
## Table Type: Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df) 
## Model: Mixed Effects (lmer)
## Response: art(mean_accuracy)
## 
##                           F Df Df.res Pr(>F)  
## 1 positionCondition 0.33588  1     11 0.5739  
## ---
## Signif. codes:   0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Factor tested: positionCondition (e.g., rotatingGlobe vs nonRotatingGlobe)
# F-statistic: 0.33588 — this tells us the ratio of variance between the groups to the variance within groups (after aligned rank transformation).
# Degrees of freedom (df): 1 for the factor, and 11 for the residuals (which likely means 12 participants).
# p-value: 0.5739 — this is not statistically significant at any common threshold (e.g., 0.05).

# An ART ANOVA revealed no significant effect of position condition (rotating vs non-rotating) on match accuracy, F(1, 11) = 0.34, p = .574.

ggplot(data.positioning.matched.accuracy_avg.long, aes(x = positionCondition, y = mean_accuracy, group = UserID)) +
  geom_line(aes(color = as.factor(UserID))) +
  geom_point(size = 3) +
  labs(title = "Paired Accuracy: Moving vs Non-Moving Globe",
       x = "Condition",
       y = "Match Accuracy") +
  theme_minimal()

ggplot(data.positioning.matched.accuracy_avg.long, aes(x = positionCondition, y = mean_accuracy)) +
  geom_boxplot(outlier.shape = NA, fill = "lightblue") +
  geom_jitter(width = 0.1, size = 2, alpha = 0.7) +
  labs(title = "Accuracy by Globe Movement Condition",
       x = "Condition",
       y = "Match Accuracy") +
  theme_minimal()

### Completion Time
data.positioning.taskCompletion_avg <- data.positioning %>%
  group_by(UserID, TaskID) %>%
  summarise(
    completion_time = as.numeric(difftime(max(Date), min(Date), units = "mins")),
    .groups = "drop"
  )

shapiro.test(data.positioning.taskCompletion_avg$completion_time)
## 
##  Shapiro-Wilk normality test
## 
## data:  data.positioning.taskCompletion_avg$completion_time
## W = 0.59479, p-value < 2.2e-16
data.positioning.taskCompletion_avg.long <- data.positioning %>%
  group_by(UserID, positionCondition, TaskID) %>%
  summarise(
    completion_time = as.numeric(difftime(max(Date), min(Date), units = "mins")),
    .groups = "drop"
  ) %>%
  group_by(UserID, positionCondition) %>%
  summarise(
    avg_completion_time = mean(completion_time),
    .groups = "drop"
  ) 
  # %>%
  # pivot_wider(names_from = positionCondition, values_from = avg_completion_time)

# wilcox.test(
#   data.positioning.taskCompletion_avg.wide$rotatingGlobe,
#   data.positioning.taskCompletion_avg.wide$nonRotatingGlobe,
#   paired = TRUE,
#   alternative = "two.sided"
# )

data.positioning.taskCompletion_avg.art <- art(avg_completion_time ~ positionCondition + (1|UserID), data = data.positioning.taskCompletion_avg.long)

anova(data.positioning.taskCompletion_avg.art)
## Analysis of Variance of Aligned Rank Transformed Data
## 
## Table Type: Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df) 
## Model: Mixed Effects (lmer)
## Response: art(avg_completion_time)
## 
##                           F Df Df.res  Pr(>F)  
## 1 positionCondition 0.26366  1     11 0.61777  
## ---
## Signif. codes:   0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# The test statistic is F(1, 11) = 0.264, with a p-value of 0.618.
# Since p > 0.05, the result is not statistically significant.
# This means that there is no evidence of a significant effect of positionCondition on avg_completion_time.
# 
# An aligned rank transform ANOVA showed that position condition did not significantly affect average task completion time, F(1, 11) = 0.26, p = .618.

# data.positioning.taskCompletion_avg.long <- data.positioning.taskCompletion_avg.wide %>%
#   pivot_longer(cols = c(rotatingGlobe, nonRotatingGlobe),
#                names_to = "Condition",
#                values_to = "completion_time")

ggplot(data.positioning.taskCompletion_avg.long, aes(x = positionCondition, y = avg_completion_time, group = UserID)) +
  geom_line(aes(color = as.factor(UserID)), linewidth = 1, alpha = 0.6) +
  geom_point(size = 3) +
  labs(
    title = "Task Completion Time by Condition",
    x = "Condition",
    y = "Completion Time (minutes)",
    color = "UserID"
  ) +
  theme_minimal()

ggplot(data.positioning.taskCompletion_avg.long, aes(x = positionCondition, y = avg_completion_time)) +
  geom_boxplot(outlier.shape = NA, fill = "lightblue") +
  geom_jitter(width = 0.1, size = 2, alpha = 0.7) +
  labs(title = "Accuracy by Globe Movement Condition",
       x = "Condition",
       y = "Match Accuracy") +
  theme_minimal()

### Subjective Measures
#### Physical Exertion

# Spearman’s rank correlation is a non-parametric test.
# It does not assume normal distribution of the variables.
# It works on ranks of the data, not the raw values — so it’s robust against skewed or non-normal distributions.

data.positioning.matched.RG <- data.positioning.matched %>% 
  filter(positionCondition == "rotatingGlobe")

cor.test(data.positioning.matched.RG$BORG_RG, 
         data.positioning.matched.RG$match_accuracy_result, method = "spearman")
## Warning in cor.test.default(data.positioning.matched.RG$BORG_RG,
## data.positioning.matched.RG$match_accuracy_result, : Cannot compute exact
## p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  data.positioning.matched.RG$BORG_RG and data.positioning.matched.RG$match_accuracy_result
## S = 3447428, p-value = 0.02285
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.1340871
ggplot(data.positioning.matched.RG, aes(x = BORG_RG, y = match_accuracy_result)) +
  geom_point(color = "steelblue", size = 2, alpha = 0.7) +
  geom_smooth(method = "loess", color = "darkred", se = TRUE) +
  labs(
    title = "Correlation between Physical Exertion and Accuracy (Rotating Globe)",
    x = "Physical Exertion (BORG_RG)",
    y = "Match Accuracy Result"
  ) +
  theme_minimal()
## `geom_smooth()` using formula = 'y ~ x'

data.positioning.matched.NRG <- data.positioning.matched %>% 
  filter(positionCondition == "nonRotatingGlobe")

cor.test(data.positioning.matched.NRG$BORG_NRG, 
         data.positioning.matched.NRG$match_accuracy_result, method = "spearman")
## Warning in cor.test.default(data.positioning.matched.NRG$BORG_NRG,
## data.positioning.matched.NRG$match_accuracy_result, : Cannot compute exact
## p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  data.positioning.matched.NRG$BORG_NRG and data.positioning.matched.NRG$match_accuracy_result
## S = 3491838, p-value = 0.03706
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.1229323
data.positioning.taskCompletion_avg.RG <- data.positioning %>%
  group_by(UserID, positionCondition, BORG_RG, TaskID) %>%
  summarise(
    completion_time = as.numeric(difftime(max(Date), min(Date), units = "mins")),
    .groups = "drop"
  ) %>%
  filter(positionCondition == "rotatingGlobe")

cor.test(data.positioning.taskCompletion_avg.RG$BORG_RG, 
         data.positioning.taskCompletion_avg.RG$completion_time, method = "spearman")
## Warning in cor.test.default(data.positioning.taskCompletion_avg.RG$BORG_RG, :
## Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  data.positioning.taskCompletion_avg.RG$BORG_RG and data.positioning.taskCompletion_avg.RG$completion_time
## S = 3103888, p-value = 0.0001632
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.2203763
data.positioning.taskCompletion_avg.NRG <- data.positioning %>%
  group_by(UserID, positionCondition, BORG_NRG, TaskID) %>%
  summarise(
    completion_time = as.numeric(difftime(max(Date), min(Date), units = "mins")),
    .groups = "drop"
  ) %>%
  filter(positionCondition == "nonRotatingGlobe")

cor.test(data.positioning.taskCompletion_avg.NRG$BORG_NRG, 
         data.positioning.taskCompletion_avg.NRG$completion_time, method = "spearman")
## Warning in cor.test.default(data.positioning.taskCompletion_avg.NRG$BORG_NRG, :
## Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  data.positioning.taskCompletion_avg.NRG$BORG_NRG and data.positioning.taskCompletion_avg.NRG$completion_time
## S = 4081328, p-value = 0.671
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##         rho 
## -0.02513373
# BORG vs Accuracy
data.positioning.matched.RG$condition <- "RG"
data.positioning.matched.NRG$condition <- "NRG"

data.positioning.matched.combined <- bind_rows(
  data.positioning.matched.RG %>% rename(BORG = BORG_RG),
  data.positioning.matched.NRG %>% rename(BORG = BORG_NRG)
)

ggplot(data.positioning.matched.combined, aes(x = BORG, y = match_accuracy_result, color = condition)) +
  geom_point(alpha = 0.7) +
  geom_smooth(method = "lm", se = TRUE) +
  labs(
    title = "Physical Exertion vs Match Accuracy",
    x = "BORG Scale",
    y = "Match Accuracy",
    color = "Condition"
  ) +
  scale_color_manual(values = c("RG" = "blue", "NRG" = "red")) +
  theme_minimal()
## `geom_smooth()` using formula = 'y ~ x'

# BORG vs Completion Time

data.positioning.taskCompletion_avg.RG$condition <- "RG"
data.positioning.taskCompletion_avg.NRG$condition <- "NRG"

data.positioning.taskCompletion_avg.combined <- bind_rows(
  data.positioning.taskCompletion_avg.RG %>% 
    rename(BORG = BORG_RG),
  data.positioning.taskCompletion_avg.NRG %>% 
    rename(BORG = BORG_NRG)
)

ggplot(data.positioning.taskCompletion_avg.combined, aes(x = BORG, y = completion_time, color = condition)) +
  geom_point(alpha = 0.7) +
  geom_smooth(method = "lm", se = TRUE) +
  labs(
    title = "Physical Exertion vs Completion Time",
    x = "BORG Scale",
    y = "Completion Time (min)",
    color = "Condition"
  ) +
  scale_color_manual(values = c("RG" = "blue", "NRG" = "red")) +
  theme_minimal()
## `geom_smooth()` using formula = 'y ~ x'

#### Preference
data.positioning %>%
  select(UserID, behaviour_preference) %>%
  distinct() %>%  
  count(behaviour_preference) %>%
  mutate(
    percent = n / sum(n),
    ncount = paste0(n, "\n", percent_format()(percent))
  ) %>%
  ggplot(aes(x = "", y = n, fill = behaviour_preference)) +
  geom_col(width = 1, color = "white") +
  coord_polar(theta = "y") +
  geom_text(aes(label = ncount), position = position_stack(vjust = 0.5), size = 4) +
  labs(
    title = "Distribution of Positioning Behaviour Preferences",
    fill = "Preference"
  ) +
  theme_void()

#### Comments
### Summary

Study: Rotating

Rotating Data Preparation

data.rotating <- data %>%
  inner_join(demographic, by = "UserID") %>%
  inner_join(rotation_OH, by = "UserID") %>%
  rename(
    PAAS_OH = Mentally_demanding,
    BORG_OH = Physically_demanding
  ) %>%
  mutate(
    PAAS_OH = as.numeric(str_extract(PAAS_OH, "\\d+(\\.\\d+)?")),
    BORG_OH = as.numeric(str_extract(BORG_OH, "\\d+(\\.\\d+)?"))
  ) %>%
  inner_join(rotation_TH, by = "UserID") %>%
  rename(
    PAAS_TH = Mentally_demanding,
    BORG_TH = Physically_demanding
  ) %>%
  mutate(
    PAAS_TH = as.numeric(str_extract(PAAS_TH, "\\d+(\\.\\d+)?")),
    BORG_TH = as.numeric(str_extract(BORG_TH, "\\d+(\\.\\d+)?"))
  ) %>%  
  inner_join(rotation_preference, by = "UserID") %>%
  rename(
    behaviour_preference = Rotation_preference,
    behaviour_feedback = Rotation_feedback
  ) %>%
  mutate(
      behaviour_preference = case_when(
    str_detect(behaviour_preference, "One-handed") ~ "oneHandedPreference",
    str_detect(behaviour_preference, "Two-handed") ~ "twoHandedPreference",
    str_detect(behaviour_preference, "no preference") ~ "noPreference",
    TRUE ~ "unknown"
  )) %>%
  filter(Type == "rotationTask") %>%
  select(UserID, TaskID, ActionID, oneHandedRotationGesture, complexity, Date, ActionStatus, main_rotation_x,
  main_rotation_y, main_rotation_z, main_rotation_w, target_rotation_x, target_rotation_y, target_rotation_z,
  target_rotation_w,match_accuracy_result, status, PAAS_OH, BORG_OH, PAAS_TH, BORG_TH, behaviour_preference, behaviour_feedback) %>%
  mutate(rotationCondition = if_else(oneHandedRotationGesture, "oneHanded", "twoHanded")) %>%
  select(-oneHandedRotationGesture) %>%
  mutate(complexity = as.factor(complexity), 
         rotationCondition = as.factor(rotationCondition),
         status = as.factor(status),
         behaviour_preference = as.factor(behaviour_preference))

Rotation Task Study

### Accuracy
#### Normality
data.rotating.matched <- data.rotating %>%
  filter(status == "Matched")

shapiro.test(data.rotating.matched$match_accuracy_result)
## 
##  Shapiro-Wilk normality test
## 
## data:  data.rotating.matched$match_accuracy_result
## W = 0.94156, p-value = 5.023e-07
hist(data.rotating.matched$match_accuracy_result, breaks = 100,
     main = "Histogram (Zoomed)", xlab = "Accuracy",
     col = "lightblue", xlim = c(0, 0.5))

plot(density(data.rotating.matched$match_accuracy_result), 
     main = "Density Plot (Zoomed)", xlab = "Accuracy",
     col = "blue", lwd = 2, xlim = c(0, 0.5))

# Although the w value is close to 1, the p value is below 0.05 so we reject null hypothesis that the data is normally distributed
# So, we cannot use one way ANOVA, instead, we use Wilcoxon signed-rank test

#### Statistical tests 
data.rotating.matched.accuracy_avg.long <- data.rotating.matched %>%
  group_by(UserID, rotationCondition) %>%
  summarise(mean_accuracy = mean(match_accuracy_result, na.rm = TRUE), .groups = 'drop') 

data.rotating.matched.art <- art(mean_accuracy ~ rotationCondition + (1|UserID), data = data.rotating.matched.accuracy_avg.long)

anova(data.rotating.matched.art)
## boundary (singular) fit: see help('isSingular')
## Analysis of Variance of Aligned Rank Transformed Data
## 
## Table Type: Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df) 
## Model: Mixed Effects (lmer)
## Response: art(mean_accuracy)
## 
##                          F Df Df.res   Pr(>F)  
## 1 rotationCondition 6.2209  1     11 0.029814 *
## ---
## Signif. codes:   0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#   Because p < .05, the result is considered statistically significant.
# Therefore, we reject the null hypothesis and conclude that rotation affects accuracy.

# An ART ANOVA revealed a significant main effect of rotation condition on mean accuracy, F(1, 11) = 6.22, p = .030, indicating that the presence of globe rotation influenced the participants’ accuracy during the task.


ggplot(data.rotating.matched.accuracy_avg.long, aes(x = rotationCondition, y = mean_accuracy, group = UserID)) +
  geom_line(aes(color = as.factor(UserID))) +
  geom_point(size = 3) +
  labs(title = "Paired Accuracy: Moving vs Non-Moving Globe",
       x = "Condition",
       y = "Match Accuracy") +
  theme_minimal()

ggplot(data.rotating.matched.accuracy_avg.long, aes(x = rotationCondition, y = mean_accuracy)) +
  geom_boxplot(outlier.shape = NA, fill = "lightblue") +
  geom_jitter(width = 0.1, size = 2, alpha = 0.7) +
  labs(title = "Accuracy by Globe Movement Condition",
       x = "Condition",
       y = "Match Accuracy") +
  theme_minimal()

### Completion Time
data.rotating.taskCompletion_avg <- data.rotating %>%
  group_by(UserID, TaskID) %>%
  summarise(
    completion_time = as.numeric(difftime(max(Date), min(Date), units = "mins")),
    .groups = "drop"
  )

shapiro.test(data.rotating.taskCompletion_avg$completion_time)
## 
##  Shapiro-Wilk normality test
## 
## data:  data.rotating.taskCompletion_avg$completion_time
## W = 0.49195, p-value < 2.2e-16
data.rotating.taskCompletion_avg.long <- data.rotating %>%
  group_by(UserID, rotationCondition, TaskID) %>%
  summarise(
    completion_time = as.numeric(difftime(max(Date), min(Date), units = "mins")),
    .groups = "drop"
  ) %>%
  group_by(UserID, rotationCondition) %>%
  summarise(
    avg_completion_time = mean(completion_time),
    .groups = "drop"
  )

data.rotating.taskCompletion_avg.art <- art(avg_completion_time ~ rotationCondition + (1|UserID), data = data.rotating.taskCompletion_avg.long)

anova(data.rotating.taskCompletion_avg.art)
## Analysis of Variance of Aligned Rank Transformed Data
## 
## Table Type: Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df) 
## Model: Mixed Effects (lmer)
## Response: art(avg_completion_time)
## 
##                          F Df Df.res   Pr(>F)  
## 1 rotationCondition 6.4517  1     11 0.027479 *
## ---
## Signif. codes:   0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# F(1, 11) = 6.45, p = 0.027
# Since the p-value is less than 0.05, the result is statistically significant.
# This means that rotationCondition has a significant effect on avg_completion_time.
# 
# An aligned rank transform ANOVA revealed a significant effect of rotation condition on task completion time, F(1, 11) = 6.45, p = .027.

ggplot(data.rotating.taskCompletion_avg.long, aes(x = rotationCondition, y = avg_completion_time, group = UserID)) +
  geom_line(aes(color = as.factor(UserID)), linewidth = 1, alpha = 0.6) +
  geom_point(size = 3) +
  labs(
    title = "Task Completion Time by Condition",
    x = "Condition",
    y = "Completion Time (minutes)",
    color = "UserID"
  ) +
  theme_minimal()

ggplot(data.rotating.taskCompletion_avg.long, aes(x = rotationCondition, y = avg_completion_time)) +
  geom_boxplot(outlier.shape = NA, fill = "lightblue") +
  geom_jitter(width = 0.1, size = 2, alpha = 0.7) +
  labs(title = "Accuracy by Globe Movement Condition",
       x = "Condition",
       y = "Match Accuracy") +
  theme_minimal()

### Subjective Measures
#### Physical Exertion
# Spearman’s rank correlation is a non-parametric test.
# It does not assume normal distribution of the variables.
# It works on ranks of the data, not the raw values — so it’s robust against skewed or non-normal distributions.

data.rotating.matched.OH <- data.rotating.matched %>% 
  filter(rotationCondition == "oneHanded")

cor.test(data.rotating.matched.OH$BORG_OH, 
         data.rotating.matched.OH$match_accuracy_result, method = "spearman")
## Warning in cor.test.default(data.rotating.matched.OH$BORG_OH,
## data.rotating.matched.OH$match_accuracy_result, : Cannot compute exact p-value
## with ties
## 
##  Spearman's rank correlation rho
## 
## data:  data.rotating.matched.OH$BORG_OH and data.rotating.matched.OH$match_accuracy_result
## S = 152593, p-value = 0.7353
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## -0.0349492
ggplot(data.rotating.matched.OH, aes(x = BORG_OH, y = match_accuracy_result)) +
  geom_point(color = "steelblue", size = 2, alpha = 0.7) +
  geom_smooth(method = "loess", color = "darkred", se = TRUE) +
  labs(
    title = "Correlation between Physical Exertion and Accuracy (Rotating Globe)",
    x = "Physical Exertion (BOOH_OH)",
    y = "Match Accuracy Result"
  ) +
  theme_minimal()
## `geom_smooth()` using formula = 'y ~ x'
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : pseudoinverse used at 1
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : neighborhood radius 1
## Warning in simpleLoess(y, x, w, span, degree = degree, parametric = parametric,
## : reciprocal condition number 5.4581e-17
## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : pseudoinverse used at 1
## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : neighborhood radius 1
## Warning in predLoess(object$y, object$x, newx = if (is.null(newdata)) object$x
## else if (is.data.frame(newdata))
## as.matrix(model.frame(delete.response(terms(object)), : reciprocal condition
## number 5.4581e-17

data.rotating.matched.TH <- data.rotating.matched %>% 
  filter(rotationCondition == "twoHanded")

cor.test(data.rotating.matched.TH$BORG_TH, 
         data.rotating.matched.TH$match_accuracy_result, method = "spearman")
## Warning in cor.test.default(data.rotating.matched.TH$BORG_TH,
## data.rotating.matched.TH$match_accuracy_result, : Cannot compute exact p-value
## with ties
## 
##  Spearman's rank correlation rho
## 
## data:  data.rotating.matched.TH$BORG_TH and data.rotating.matched.TH$match_accuracy_result
## S = 133640, p-value = 0.3644
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## 0.09359448
data.rotating.taskCompletion_avg.OH <- data.rotating %>%
  group_by(UserID, rotationCondition, BORG_OH, TaskID) %>%
  summarise(
    completion_time = as.numeric(difftime(max(Date), min(Date), units = "mins")),
    .groups = "drop"
  ) %>%
  filter(rotationCondition == "oneHanded")

cor.test(data.rotating.taskCompletion_avg.OH$BORG_OH, 
         data.rotating.taskCompletion_avg.OH$completion_time, method = "spearman")
## Warning in cor.test.default(data.rotating.taskCompletion_avg.OH$BORG_OH, :
## Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  data.rotating.taskCompletion_avg.OH$BORG_OH and data.rotating.taskCompletion_avg.OH$completion_time
## S = 139512, p-value = 0.6029
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##        rho 
## 0.05376892
data.rotating.taskCompletion_avg.TH <- data.rotating %>%
  group_by(UserID, rotationCondition, BORG_TH, TaskID) %>%
  summarise(
    completion_time = as.numeric(difftime(max(Date), min(Date), units = "mins")),
    .groups = "drop"
  ) %>%
  filter(rotationCondition == "twoHanded")

cor.test(data.rotating.taskCompletion_avg.TH$BORG_TH, 
         data.rotating.taskCompletion_avg.TH$completion_time, method = "spearman")
## Warning in cor.test.default(data.rotating.taskCompletion_avg.TH$BORG_TH, :
## Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  data.rotating.taskCompletion_avg.TH$BORG_TH and data.rotating.taskCompletion_avg.TH$completion_time
## S = 131579, p-value = 0.2968
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.1075767
# BORG vs Accuracy

data.rotating.matched.OH$condition <- "OH"
data.rotating.matched.TH$condition <- "TH"

data.rotating.matched.combined <- bind_rows(
  data.rotating.matched.OH %>% rename(BORG = BORG_OH),
  data.rotating.matched.TH %>% rename(BORG = BORG_TH)
)

ggplot(data.rotating.matched.combined, aes(x = BORG, y = match_accuracy_result, color = condition)) +
  geom_point(alpha = 0.7) +
  geom_smooth(method = "lm", se = TRUE) +
  labs(
    title = "Physical Exertion vs Match Accuracy",
    x = "BORG Scale",
    y = "Match Accuracy",
    color = "Condition"
  ) +
  scale_color_manual(values = c("OH" = "blue", "TH" = "red")) +
  theme_minimal()
## `geom_smooth()` using formula = 'y ~ x'

# BORG vs Completion Time

data.rotating.taskCompletion_avg.OH$condition <- "OH"
data.rotating.taskCompletion_avg.TH$condition <- "TH"

data.rotating.taskCompletion_avg.combined <- bind_rows(
  data.rotating.taskCompletion_avg.OH %>% 
    rename(BORG = BORG_OH),
  data.rotating.taskCompletion_avg.TH %>% 
    rename(BORG = BORG_TH)
)

ggplot(data.rotating.taskCompletion_avg.combined, aes(x = BORG, y = completion_time, color = condition)) +
  geom_point(alpha = 0.7) +
  geom_smooth(method = "lm", se = TRUE) +
  labs(
    title = "Physical Exertion vs Completion Time",
    x = "BORG Scale",
    y = "Completion Time (min)",
    color = "Condition"
  ) +
  scale_color_manual(values = c("OH" = "blue", "TH" = "red")) +
  theme_minimal()
## `geom_smooth()` using formula = 'y ~ x'

#### Preference
data.rotating %>%
  select(UserID, behaviour_preference) %>%
  distinct() %>%  
  count(behaviour_preference) %>%
  mutate(
    percent = n / sum(n),
    ncount = paste0(n, "\n", percent_format()(percent))
  ) %>%
  ggplot(aes(x = "", y = n, fill = behaviour_preference)) +
  geom_col(width = 1, color = "white") +
  coord_polar(theta = "y") +
  geom_text(aes(label = ncount), position = position_stack(vjust = 0.5), size = 4) +
  labs(
    title = "Distribution of Rotation Behaviour Preferences",
    fill = "Preference"
  ) +
  theme_void()

#### Comments
### Summary

Study: Scale

Scale Data Preparation

data.scale <- data %>%
  inner_join(demographic, by = "UserID") %>%
  inner_join(scale_MG, by = "UserID") %>%
    rename(
    PAAS_MG = Mentally_demanding,
    BORG_MG = Physically_demanding
  ) %>%
  mutate(
    PAAS_MG = as.numeric(str_extract(PAAS_MG, "\\d+(\\.\\d+)?")),
    BORG_MG = as.numeric(str_extract(BORG_MG, "\\d+(\\.\\d+)?"))
  ) %>%
  inner_join(scale_NMG, by = "UserID") %>%
  rename(
    PAAS_NMG = Mentally_demanding,
    BORG_NMG = Physically_demanding
  ) %>%
  mutate(
    PAAS_NMG = as.numeric(str_extract(PAAS_NMG, "\\d+(\\.\\d+)?")),
    BORG_NMG = as.numeric(str_extract(BORG_NMG, "\\d+(\\.\\d+)?"))
  ) %>%
  inner_join(scale_preference, by = "UserID") %>%
  rename(
    behaviour_preference = Scale_preference,
    behaviour_feedback = Scale_feedback
  ) %>%
  mutate(
    behaviour_preference = case_when(
    str_detect(behaviour_preference, "Maintain distance") ~ "maintainDistance",
    str_detect(behaviour_preference, "Maintain globe") ~ "maintainGlobe",
    str_detect(behaviour_preference, "no preference") ~ "noPreference",
    TRUE ~ "unknown"
  )) %>%
  filter(Type == "scaleTask") %>%
  select(UserID, TaskID, ActionID, moveGlobeWhileScaling, zoomDirection, Date, ActionStatus, main_scale_x,
  main_scale_y, main_scale_z, target_scale_x, target_scale_y, target_scale_z, match_accuracy_result, status,
  PAAS_MG, BORG_MG, PAAS_NMG, BORG_NMG, behaviour_preference, behaviour_feedback) %>%
  mutate(scaleCondition = if_else(moveGlobeWhileScaling, "movingGlobe", "nonMovingGlobe")) %>%
  select(-moveGlobeWhileScaling) %>%
  mutate(zoomDirection = as.factor(zoomDirection), 
         scaleCondition = as.factor(scaleCondition),
         status = as.factor(status),
         behaviour_preference = as.factor(behaviour_preference))

Scale Task Study

### Accuracy
#### Normality
data.scale.matched <- data.scale %>%
  filter(status == "Matched")

shapiro.test(data.scale.matched$match_accuracy_result)
## 
##  Shapiro-Wilk normality test
## 
## data:  data.scale.matched$match_accuracy_result
## W = 0.94732, p-value = 1.64e-06
hist(data.scale.matched$match_accuracy_result, breaks = 100,
     main = "Histogram (Zoomed)", xlab = "Accuracy",
     col = "lightblue", xlim = c(0, 0.5))

plot(density(data.scale.matched$match_accuracy_result), 
     main = "Density Plot (Zoomed)", xlab = "Accuracy",
     col = "blue", lwd = 2, xlim = c(0, 0.5))

# Although the w value is close to 1, the p value is below 0.05 so we reject null hypothesis that the data is normally distributed
# So, we cannot use one way ANOVA, instead, we use Wilcoxon signed-rank test

#### Statistical tests 
data.scale.matched.accuracy_avg.long <- data.scale.matched %>%
  group_by(UserID, scaleCondition) %>%
  summarise(mean_accuracy = mean(match_accuracy_result, na.rm = TRUE), .groups = 'drop')
  # %>%
  # pivot_wider(names_from = scaleCondition, values_from = mean_accuracy)

data.scale.matched.art <- art(mean_accuracy ~ scaleCondition + (1|UserID), data = data.scale.matched.accuracy_avg.long)

anova(data.scale.matched.art)
## Analysis of Variance of Aligned Rank Transformed Data
## 
## Table Type: Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df) 
## Model: Mixed Effects (lmer)
## Response: art(mean_accuracy)
## 
##                        F Df Df.res Pr(>F)  
## 1 scaleCondition 0.43825  1     11 0.5216  
## ---
## Signif. codes:   0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Because p > .05, we fail to reject the null hypothesis.
# There is no evidence that changing the scale condition affects how accurately participants performed.

# An ART ANOVA showed no significant effect of scale condition on mean accuracy, F(1, 11) = 0.44, p = .522, indicating that changing the scale of the globe did not impact participants’ accuracy.

ggplot(data.scale.matched.accuracy_avg.long, aes(x = scaleCondition, y = mean_accuracy, group = UserID)) +
  geom_line(aes(color = as.factor(UserID))) +
  geom_point(size = 3) +
  labs(title = "Paired Accuracy: Moving vs Non-Moving Globe",
       x = "Condition",
       y = "Match Accuracy") +
  theme_minimal()

ggplot(data.scale.matched.accuracy_avg.long, aes(x = scaleCondition, y = mean_accuracy)) +
  geom_boxplot(outlier.shape = NA, fill = "lightblue") +
  geom_jitter(width = 0.1, size = 2, alpha = 0.7) +
  labs(title = "Accuracy by Globe Movement Condition",
       x = "Condition",
       y = "Match Accuracy") +
  theme_minimal()

### Completion Time
data.scale.taskCompletion_avg <- data.scale %>%
  group_by(UserID, TaskID) %>%
  summarise(
    completion_time = as.numeric(difftime(max(Date), min(Date), units = "mins")),
    .groups = "drop"
  )

shapiro.test(data.scale.taskCompletion_avg$completion_time)
## 
##  Shapiro-Wilk normality test
## 
## data:  data.scale.taskCompletion_avg$completion_time
## W = 0.69808, p-value < 2.2e-16
data.scale.taskCompletion_avg.long <- data.scale %>%
  group_by(UserID, scaleCondition, TaskID) %>%
  summarise(
    completion_time = as.numeric(difftime(max(Date), min(Date), units = "mins")),
    .groups = "drop"
  ) %>%
  group_by(UserID, scaleCondition) %>%
  summarise(
    avg_completion_time = mean(completion_time),
    .groups = "drop"
  ) 
  # %>%
  # pivot_wider(names_from = scaleCondition, values_from = avg_completion_time)

data.scale.taskCompletion.art <- art(avg_completion_time ~ scaleCondition + (1|UserID), data = data.scale.taskCompletion_avg.long)

anova(data.scale.matched.art)
## Analysis of Variance of Aligned Rank Transformed Data
## 
## Table Type: Analysis of Deviance Table (Type III Wald F tests with Kenward-Roger df) 
## Model: Mixed Effects (lmer)
## Response: art(mean_accuracy)
## 
##                        F Df Df.res Pr(>F)  
## 1 scaleCondition 0.43825  1     11 0.5216  
## ---
## Signif. codes:   0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# An aligned rank transform (ART) ANOVA revealed no significant effect of scale condition on mean accuracy, F(1, 11) = 0.44, p = .522.
# There was no significant effect of scale condition on mean accuracy, F(1, 11) = 0.44, p = .522.

# wilcox.test(
#   data.scale.taskCompletion_avg.wide$movingGlobe,
#   data.scale.taskCompletion_avg.wide$nonMovingGlobe,
#   paired = TRUE,
#   alternative = "two.sided"
# )

# data.scale.taskCompletion_avg.long <- data.scale.taskCompletion_avg.wide %>%
#   pivot_longer(cols = c(movingGlobe, nonMovingGlobe),
#                names_to = "Condition",
#                values_to = "completion_time")

ggplot(data.scale.taskCompletion_avg.long, aes(x = scaleCondition, y = avg_completion_time, group = UserID)) +
  geom_line(aes(color = as.factor(UserID)), linewidth = 1, alpha = 0.6) +
  geom_point(size = 3) +
  labs(
    title = "Task Completion Time by Condition",
    x = "Condition",
    y = "Completion Time (minutes)",
    color = "UserID"
  ) +
  theme_minimal()

ggplot(data.scale.taskCompletion_avg.long, aes(x = scaleCondition, y = avg_completion_time)) +
  geom_boxplot(outlier.shape = NA, fill = "lightblue") +
  geom_jitter(width = 0.1, size = 2, alpha = 0.7) +
  labs(title = "Accuracy by Globe Movement Condition",
       x = "Condition",
       y = "Match Accuracy") +
  theme_minimal()

### Subjective Measures
#### Physical Exertion
# Spearman’s rank correlation is a non-parametric test.
# It does not assume normal distribution of the variables.
# It works on ranks of the data, not the raw values — so it’s robust against skewed or non-normal distributions.

data.positioning.matched.RG <- data.positioning.matched %>% 
  filter(positionCondition == "rotatingGlobe")

cor.test(data.positioning.matched.RG$BORG_RG, 
         data.positioning.matched.RG$match_accuracy_result, method = "spearman")
## Warning in cor.test.default(data.positioning.matched.RG$BORG_RG,
## data.positioning.matched.RG$match_accuracy_result, : Cannot compute exact
## p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  data.positioning.matched.RG$BORG_RG and data.positioning.matched.RG$match_accuracy_result
## S = 3447428, p-value = 0.02285
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.1340871
ggplot(data.positioning.matched.RG, aes(x = BORG_RG, y = match_accuracy_result)) +
  geom_point(color = "steelblue", size = 2, alpha = 0.7) +
  geom_smooth(method = "loess", color = "darkred", se = TRUE) +
  labs(
    title = "Correlation between Physical Exertion and Accuracy (Rotating Globe)",
    x = "Physical Exertion (BORG_RG)",
    y = "Match Accuracy Result"
  ) +
  theme_minimal()
## `geom_smooth()` using formula = 'y ~ x'

data.positioning.matched.NRG <- data.positioning.matched %>% 
  filter(positionCondition == "nonRotatingGlobe")

cor.test(data.positioning.matched.NRG$BORG_NRG, 
         data.positioning.matched.NRG$match_accuracy_result, method = "spearman")
## Warning in cor.test.default(data.positioning.matched.NRG$BORG_NRG,
## data.positioning.matched.NRG$match_accuracy_result, : Cannot compute exact
## p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  data.positioning.matched.NRG$BORG_NRG and data.positioning.matched.NRG$match_accuracy_result
## S = 3491838, p-value = 0.03706
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.1229323
data.positioning.taskCompletion_avg.RG <- data.positioning %>%
  group_by(UserID, positionCondition, BORG_RG, TaskID) %>%
  summarise(
    completion_time = as.numeric(difftime(max(Date), min(Date), units = "mins")),
    .groups = "drop"
  ) %>%
  filter(positionCondition == "rotatingGlobe")

cor.test(data.positioning.taskCompletion_avg.RG$BORG_RG, 
         data.positioning.taskCompletion_avg.RG$completion_time, method = "spearman")
## Warning in cor.test.default(data.positioning.taskCompletion_avg.RG$BORG_RG, :
## Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  data.positioning.taskCompletion_avg.RG$BORG_RG and data.positioning.taskCompletion_avg.RG$completion_time
## S = 3103888, p-value = 0.0001632
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.2203763
data.positioning.taskCompletion_avg.NRG <- data.positioning %>%
  group_by(UserID, positionCondition, BORG_NRG, TaskID) %>%
  summarise(
    completion_time = as.numeric(difftime(max(Date), min(Date), units = "mins")),
    .groups = "drop"
  ) %>%
  filter(positionCondition == "nonRotatingGlobe")

cor.test(data.positioning.taskCompletion_avg.NRG$BORG_NRG, 
         data.positioning.taskCompletion_avg.NRG$completion_time, method = "spearman")
## Warning in cor.test.default(data.positioning.taskCompletion_avg.NRG$BORG_NRG, :
## Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  data.positioning.taskCompletion_avg.NRG$BORG_NRG and data.positioning.taskCompletion_avg.NRG$completion_time
## S = 4081328, p-value = 0.671
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##         rho 
## -0.02513373
# BORG vs Accuracy
ggplot(data.positioning.matched.RG, aes(x = BORG_RG, y = match_accuracy_result)) +
  geom_point() +
  geom_smooth(method = "lm", se = TRUE) +
  labs(title = "Physical Exertion vs Match Accuracy", x = "BORG Scale (RG)", y = "Match Accuracy")
## `geom_smooth()` using formula = 'y ~ x'

ggplot(data.positioning.matched.NRG, aes(x = BORG_NRG, y = match_accuracy_result)) +
  geom_point() +
  geom_smooth(method = "lm", se = TRUE) +
  labs(title = "Physical Exertion vs Match Accuracy", x = "BORG Scale (RG)", y = "Match Accuracy")
## `geom_smooth()` using formula = 'y ~ x'

data.positioning.matched.RG$condition <- "RG"
data.positioning.matched.NRG$condition <- "NRG"

data.positioning.matched.combined <- bind_rows(
  data.positioning.matched.RG %>% rename(BORG = BORG_RG),
  data.positioning.matched.NRG %>% rename(BORG = BORG_NRG)
)

ggplot(data.positioning.matched.combined, aes(x = BORG, y = match_accuracy_result, color = condition)) +
  geom_point(alpha = 0.7) +
  geom_smooth(method = "lm", se = TRUE) +
  labs(
    title = "Physical Exertion vs Match Accuracy",
    x = "BORG Scale",
    y = "Match Accuracy",
    color = "Condition"
  ) +
  scale_color_manual(values = c("RG" = "blue", "NRG" = "red")) +
  theme_minimal()
## `geom_smooth()` using formula = 'y ~ x'

# BORG vs Completion Time

data.positioning.taskCompletion_avg.RG$condition <- "RG"
data.positioning.taskCompletion_avg.NRG$condition <- "NRG"

data.positioning.taskCompletion_avg.combined <- bind_rows(
  data.positioning.taskCompletion_avg.RG %>% 
    rename(BORG = BORG_RG),
  data.positioning.taskCompletion_avg.NRG %>% 
    rename(BORG = BORG_NRG)
)

ggplot(data.positioning.taskCompletion_avg.combined, aes(x = BORG, y = completion_time, color = condition)) +
  geom_point(alpha = 0.7) +
  geom_smooth(method = "lm", se = TRUE) +
  labs(
    title = "Physical Exertion vs Completion Time",
    x = "BORG Scale",
    y = "Completion Time (min)",
    color = "Condition"
  ) +
  scale_color_manual(values = c("RG" = "blue", "NRG" = "red")) +
  theme_minimal()
## `geom_smooth()` using formula = 'y ~ x'

#### Preference
data.scale %>%
  select(UserID, behaviour_preference) %>%
  distinct() %>%  
  count(behaviour_preference) %>%
  mutate(
    percent = n / sum(n),
    ncount = paste0(n, "\n", percent_format()(percent))
  ) %>%
  ggplot(aes(x = "", y = n, fill = behaviour_preference)) +
  geom_col(width = 1, color = "white") +
  coord_polar(theta = "y") +
  geom_text(aes(label = ncount), position = position_stack(vjust = 0.5), size = 4) +
  labs(
    title = "Distribution of Scale Behaviour Preferences",
    fill = "Preference"
  ) +
  theme_void()

#### Comments
### Summary